Paper Info
Title
Finding trending local topics in search queries for personalization of a recommendation system
Abstract
In this paper, we present our approach for geographic personalization of a content recommendation system. More specifically, our work focuses on recommending query topics to users. We do this by mining the search query logs to detect trending local topics. For a set of queries we compute their counts and what we call buzz scores, which is a metric for detecting trending behavior. We also compute the entropy of the geographic distribution of the queries as means of detecting their location affinity. We cluster the queries into trending topics and assign the topics to their corresponding location. Human editors then select a subset of these local topics and enter them into a recommendation system. In turn the recommendation system optimizes a pool of trending local and global topics by exploiting user feedback. We present some editorial evaluation of the technique and results of a live experiment. Inclusion of local topics in selected locations into the global pool of topics resulted in more than 6% relative increase in user engagement with the recommendation system compared to using the global topics exclusively.
Year
DOI
Venue
2012
10.1145/2339530.2339594
KDD
Keywords
Field
DocType
geographic distribution,content recommendation system,local topic,recommendation system,search query,query topic,global topic,corresponding location,location affinity,global pool,geographic personalization,clustering,recommender system,personalization
Recommender system,Web search query,Data mining,World Wide Web,Geographic distribution,Information retrieval,Computer science,User engagement,Cluster analysis,Marketing buzz,Personalization
Conference
Citations 
PageRank 
References 
14
0.66
14
Authors
3
Name
Order
Citations
PageRank
Ziad Al Bawab1252.93
George H. Mills2140.66
Jean-Francois Crespo3181.74